Biometric matching is a reliable and widely used technique for personal identification or verification. As understood by those skilled in the art, biometrics is the statistical study of biological data such as retina or iris patterns, fingerprint patterns, facial shape or patterns, cornea patterns, hand geometry, or voice patterns. The use of biometric data can be an effective way to prevent unauthorized use of information resources, equipment, confidential data, vehicles, identification cards, credit/debit cards, and other items or data desired to be protected.
For example, a common approach to fingerprint identification involves scanning a sample fingerprint or an image thereof, converting it into electrical signals, and storing the image and/or unique characteristics of the fingerprint image. The characteristics of a sample fingerprint may be compared to information for reference fingerprints already in storage to determine identification or for verification purposes.
Even though advancements in the use of fingerprint data have been made over the years, comparing a sample fingerprint to a large number of reference fingerprints may be prohibitively expensive and/or simply take too long. Accordingly, fingerprints are typically classified into a plurality of discrete sets and/or subsets in the form of a hierarchical tree to thereby expedite searching. A common top level classification for fingerprints, for example, usually differentiates the prints into the classes of: plain whorl, plain loop, tented arch, etc. based upon broad ridge pattern types. These classes may be yet further divided into subclasses. Accordingly, a fingerprint sample to be searched, once itself is classified, can be more efficiently compared to only those prints in the respective classes and subclasses of the search tree. For example, U.S. Pat. No. 5,465,303 to Levison et al. describes both the widely used Henry classification system and the Vucetich classification system.
The conventional classification approaches may result in binning of fingerprint types or classifications, wherein a single bin is searched for the matching print. Nevertheless, the bins may still be relatively large thus resulting in lengthy and expensive searching. Moreover, a print is considered either in one bin or another and there is no consideration given to any uncertainty in whether the print is in one bin or another. In other words, there is typically no relation between adjacent bins that would assist in searching. Any degradation in the print quality may also result in it being simply in the wrong bin and thus unmatchable.
Fingerprint minutiae, the branches or bifurcations and end points of the fingerprint ridges, are often used to determine a match between a sample print and a reference print database. For example, U.S. Pat. Nos. 3,859,633 and 3,893,080 both to Ho et al. are directed to fingerprint identification based upon fingerprint minutiae matching.
U.S. Pat. No. 3,959,884 to Jordan et al. discloses a method of classifying fingerprints by converting a fingerprint to a pattern of binary values which define a spatial array of ridges and valleys. A part of the pattern is selected for transformation by a repeatable criteria, such as a circular area having its center at the average center of curvature of the circular ridges of the upper part of the print. The data represented by the selected part is transformed into a plot of a relative position/intensity surface with a predetermined set of first and second coordinates and a variable third coordinate having a value dependent upon the frequency of occurrence of ridges when the origin of the first and second coordinates is taken at multiple positions over the selected area. The surface is divided into multiple areas, the number of areas being equal to the desired number of code elements in a descriptor code to be constructed. The curvature of the surface within each area is preferably quantized to produce the number of code elements. Finally, a descriptor code is constructed by concatenating the code elements in a predetermined order.
Yet another approach to fingerprint matching attempts to assign a unique digital code to each fingerprint. For example, U.S. Pat. No. 4,747,147 to Sparrow discloses a fingerprint scanning system and method for rotating a scan line about a central point on the fingerprint. A code representing the types of irregularities is recorded, along with a ridge count so that coordinates give a complete topological and spatial description of a fingerprint for computer processing.
Other approaches are also known for attempting to efficiently and accurately find a match between a sample fingerprint and a database of reference prints. For example, U.S. Pat. No. 5,239,590 to Yamamoto discloses a fingerprint image processing method wherein a master and a sample fingerprint image are divided into a plurality of blocks and each block is divided into a plurality of block areas, in turn, having a plurality of pixels with an associated direction. The direction of each pixel is determined based on pixel density partial differentials between the pixel and adjacent pixels for a plurality of directions. A match is determined based upon specific dispersion, mean, and cross-correlation calculations.
Despite the proliferation of and attempted advancements in fingerprint and other biometric data classifications and searching systems and methods, storing, searching, and processing biometric data, especially for large databases, remains cumbersome and time consuming. Additionally, many of these known systems are limited to only one user performing the searching capabilities at a designated searching terminal. Accordingly, there still exists a need for reliable, efficient, and readily expandable automated biometric data storage, searching, and matching.